Aboul Ella Hassanien (Abo)

Image segmentation is vital for meaningful analysis and interpretation
of medical images. The most popular method for clustering is k-means
clustering. This article presents a new approach intended to provide more reliable
Magnetic Resonance (MR) breast image segmentation that is based on
adaptation to identify target objects through an optimization methodology
that maintains the optimum result during iterations. The proposed approach
improves and enhances the effectiveness and efficiency of the traditional kmeans
clustering algorithm. The performance of the presented approach was
evaluated using various tests and different MR breast images. The experimental
results demonstrate that the overall accuracy provided by the proposed
adaptive k-means approach is superior to the standard k-means clustering
technique.

Image segmentation is an important task in the image processing
field. Efficient segmentation of images considered important for further object
recognition and classification. This paper presents a novel segmentation
approach based on Particle Swarm Optimization (PSO) and an adaptive
Watershed algorithm. An application of liver CT imaging has been chosen and
PSO approach has been applied to segment abdominal CT images. The
experimental results show the efficiency of the proposed approach and it
obtains overall accuracy 94% of good liver extraction.

In this paper, we propose a novel optimal singular value decomposition (SVD)-based image watermarking approach that uses a new combination of weighted quantum particle swarm optimization (WQPSO) algorithm and a human visual system (HVS) model for both the hybrid discrete wavelet transform and discrete cosine transform (DCT). The proposed SVD-based watermarking approach initially decomposes the host image into sub-bands; afterwards, singular values of the DCT of the lower sub-band of the host image are quantized using a set of optimal quantization steps deduced from a combination of the WQPSO algorithm and the HVS model. To evaluate the performance of the proposed approach, we present tests on different images. The experimental results show that the proposed approach yields a watermarked image with good visual definition; at the same time, the embedded watermark was robust against a wide variety of common attacks, including JPEG compression, Gaussian noise, salt and pepper noises, Gaussian filters, median filters, image cropping, and image scaling. Moreover, the results of various experimental analyses demonstrated the superiority of the WQPSO approach over other optimization techniques, including classical PSO and QPSO in terms of local convergence speed, resulting in a better balance between global and local searches of the watermarking algorithm.

In this paper we present a secure patient medical images and authentication scheme which enhances the security, confidentiality and integrity of medical images transmitted through the Internet. This paper proposes a watermarking by invoking particle swarm optimization (PSO) technique in adaptive quantization index modulation and singular value decomposition in conjunction with discrete wavelet transform (DWT) and discrete cosine transform (DCT). The proposed approach promotes the robustness and watermarked image quality. The experimental results show that the proposed algorithm yields a watermark which is invisible to human eyes, robust against a wide variety of common attacks and reliable enough for tracing colluders.